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We created staged logistic regression models, adjusting for sociodemographic characteristics and self-reported racial/ethnic discrimination.
Both predictors were influential in models adjusting for each other.
Data were analyzed using Weibull models, adjusting for age, sex, ART regimen, CD4 cell count, clinical stage and treatment programme.
General linear models adjusting for age served to evaluate the impact of PCOS on clinical and biochemical variables.
Marginal Cox-regression models adjusting for surgeon clustering were used.
We fit multivariable models adjusting for various potential confounders.
Models adjusting for age, sex, and comorbidity showed similar results.
We used three models adjusting for possible confounding factors.
Models adjusting for urban and rural indicators also did not modify the reported results.
We fitted all models adjusting for age and for other possible confounding variables.
Models adjusting for age (5 year categories), calendar year and marital status were fitted.
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